Research Synthesis Methods

A framework for identifying treatment‐covariate interactions in individual participant data network meta‐analysis

Journal Article

Background: Stratified medicine seeks to identify patients most likely to respond to treatment. Individual participant data (IPD) network meta‐analysis (NMA) models have greater power than individual trials to identify treatment‐covariate interactions (TCIs). Treatment‐covariate interactions contain “within” and “across” trial interactions, where the across‐trial interaction is more susceptible to confounding and ecological bias.

Methods: We considered a network of IPD from 37 trials (5922 patients) for cervical cancer (2394 events), where previous research identified disease stage as a potential interaction covariate. We compare 2 models for NMA with TCIs: (1) 2 effects separating within‐ and across‐trial interactions and (2) a single effect combining within‐ and across‐trial interactions. We argue for a visual assessment of consistency of within‐ and across‐trial interactions and consider more detailed aspects of interaction modelling, eg, common vs trial‐specific effects of the covariate. This leads us to propose a practical framework for IPD NMA with TCIs.

Results: Following our framework, we found no evidence in the cervical cancer network for a treatment‐stage interaction on the basis of the within‐trial interaction. The NMA provided additional power for an across‐trial interaction over and above the pairwise evidence. Following our proposed framework, we found that the within‐ and across‐trial interactions should not be combined.

Conclusion: Across‐trial interactions are susceptible to confounding and ecological bias. It is important to separate the sources of evidence to check their consistency and identify which sources of evidence are driving the conclusion. Our framework provides practical guidance for researchers, reducing the risk of unduly optimistic interpretation of TCIs.

Related Topics

Related Publications

Related Content

Site Footer

Address:

This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on StatisticsViews.com are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and StatisticsViews.com express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.